LoRA - Revision history

CUA: /* LoRA */

2025-09-18T10:20:53Z

LoRA

← Older revision		Revision as of 12:20, 18 September 2025
Line 104:		Line 104:

	[15] extrafuzzy, "How I made over $1000 in 3 months selling LoRAs", ''Civitai'', published October 22, 2023, accessed August 21, 2025, https://civitai.com/articles/2684/how-i-made-over-dollar1000-in-3-months-selling-loras.		[15] extrafuzzy, "How I made over $1000 in 3 months selling LoRAs", ''Civitai'', published October 22, 2023, accessed August 21, 2025, https://civitai.com/articles/2684/how-i-made-over-dollar1000-in-3-months-selling-loras.

			== Guestbook ==
			<eplite id="Objects_of_interest_and_necessity_LoRA" height="600px" width="1000px" />

	[[Category:Objects of Interest and Necessity]]		[[Category:Objects of Interest and Necessity]]

NicolasMaleve: /* Negotiating the boundaries of property and identity */

2025-09-12T15:22:57Z

Negotiating the boundaries of property and identity

← Older revision		Revision as of 17:22, 12 September 2025
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	==== Negotiating the boundaries of property and identity ====		==== Negotiating the boundaries of property and identity ====
	Curation involve ethical decisions that are often mentioned in tutorials and posts. Is it OK to scrape from any source? When is appropriation legitimate? The implications of these questions become apparent when users have to take position for or against making style LoRAs. A style LoRA is a small adapter that can be included in a generative pipeline in order to make images in the style of a given artist. Fans are caught between their desire to engage more deeply with the universe of their favourite authors, but they are also aware that they might infringe on their right. In fan communities, to reproduce a character is a well-accepted practice. The site ~~DevianArt~~ features endless crowds of unicorns made by anime lovers. To enable style mimicry is perceived as going a step further. This time is to substitute oneself to the author. A dilemma of the same kind occurs when users produce LoRAs that either make it easier to generate realistic images or that make it possible to generate a convincing representation of an existing person. The production of deepfake is often prohibited in the notice that accompanies models. Nevertheless, a quick search on CivitAI reveals an impressive number of LoRAs impersonating actors, politicians and porn stars. Which brings the platform to the limit of legality.		Curation involve ethical decisions that are often mentioned in tutorials and posts. Is it OK to scrape from any source? When is appropriation legitimate? The implications of these questions become apparent when users have to take position for or against making style LoRAs. A style LoRA is a small adapter that can be included in a generative pipeline in order to make images in the style of a given artist. Fans are caught between their desire to engage more deeply with the universe of their favourite authors, but they are also aware that they might infringe on their right. In fan communities, to reproduce a character is a well-accepted practice. The site DeviantArt features endless crowds of unicorns made by anime lovers. To enable style mimicry is perceived as going a step further. This time is to substitute oneself to the author. A dilemma of the same kind occurs when users produce LoRAs that either make it easier to generate realistic images or that make it possible to generate a convincing representation of an existing person. The production of deepfake is often prohibited in the notice that accompanies models. Nevertheless, a quick search on CivitAI reveals an impressive number of LoRAs impersonating actors, politicians and porn stars. Which brings the platform to the limit of legality.

	==== Another distribution of labour ====		==== Another distribution of labour ====

CUA: /* Another distribution of labour */

2025-08-26T10:38:16Z

Another distribution of labour

← Older revision		Revision as of 12:38, 26 August 2025
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	==== Another distribution of labour ====		==== Another distribution of labour ====
	The curatorial practice of LoRA creators is very different than the one used to assemble large-scale datasets for foundational models. The curators of huge datasets such as [[LAION]] privilege broad scraping and an automatic process. In the case of LoRAs, the creators are manually picking, and, even when they resort to scraping, they visually monitor the results. Each individual image is validated by the curator. Further, this curatorial practice forms part of an integrated whole. The same person selects the images, writes the captions and trains the LoRA. This again sets the mode of production of LoRAs apart from big models' where separate entities deal with the dataset's curation and the training of the model. But we should not simply oppose the craft of LoRA curators to the industrial approach of large dataset creators as they depend on each other. Either for their mere existence~~, -~~ no LoRA without underlying model - , or to gain value~~, -~~ a large model becomes more popular if a LoRA extends its capabilities.		The curatorial practice of LoRA creators is very different than the one used to assemble large-scale datasets for foundational models. The curators of huge datasets such as [[LAION]] privilege broad scraping and an automatic process. In the case of LoRAs, the creators are manually picking, and, even when they resort to scraping, they visually monitor the results. Each individual image is validated by the curator. Further, this curatorial practice forms part of an integrated whole. The same person selects the images, writes the captions and trains the LoRA. This again sets the mode of production of LoRAs apart from big models' where separate entities deal with the dataset's curation and the training of the model. But we should not simply oppose the craft of LoRA curators to the industrial approach of large dataset creators as they depend on each other. Either for their mere existence (no LoRA without underlying model) , or to gain value (a large model becomes more popular if a LoRA extends its capabilities).
	==== Baking the cake ====		==== Baking the cake ====
	When the dataset is ready, the training phase begins. Training solicits a different set of skills and knowledge. If some users quickly put together a collection of images and train the LoRA with default parameters while hoping for the best, experienced users delve into a vertiginous list of parameters to 'bake the perfect cake'. To achieve this goal, they must strike a balance between the ideal amount of training and the available material infrastructure, as well as the time during which the machine can be 'monopolized' by the training process. On a personal laptop, better results can be obtained with 24 hours of training. But this means that the machine won't be available for that time. And 24 hours might be long to check the results. Training is time management combined with an evaluation of resource availability that the user must learn to translate into a selection of arcane parameters such as epochs, batch size and steps. The user faces another challenge: the problem of 'overcooking'. A LoRA is 'overcooked' when it reproduces too literally its training content. In the computer science jargon, this effect called ''overfitting'' is correlated to a selection of parameters that make the model memorize the noise and other irrelevant information from the dataset.[10] When making a LoRA for adding the representation of a particular object in the model, an example of overfitting would be that the model would not only generate images with this object, but also include elements of the background of the training images disrupting the visual consistency of the newly created picture. For many, baking a LoRA is an art more than a science and the choice of parameters is influenced by reports circulating on an extensive network of platforms that range from Reddit and [[CivitAI]] to GitHub and [[Hugging Face]] to Arxiv. At the training stage, the network extends from the sites that provide images and curatorial resources to a mix of highly technical pages and sites where informal conversation can be had.		When the dataset is ready, the training phase begins. Training solicits a different set of skills and knowledge. If some users quickly put together a collection of images and train the LoRA with default parameters while hoping for the best, experienced users delve into a vertiginous list of parameters to 'bake the perfect cake'. To achieve this goal, they must strike a balance between the ideal amount of training and the available material infrastructure, as well as the time during which the machine can be 'monopolized' by the training process. On a personal laptop, better results can be obtained with 24 hours of training. But this means that the machine won't be available for that time. And 24 hours might be long to check the results. Training is time management combined with an evaluation of resource availability that the user must learn to translate into a selection of arcane parameters such as epochs, batch size and steps. The user faces another challenge: the problem of 'overcooking'. A LoRA is 'overcooked' when it reproduces too literally its training content. In the computer science jargon, this effect called ''overfitting'' is correlated to a selection of parameters that make the model memorize the noise and other irrelevant information from the dataset.[10] When making a LoRA for adding the representation of a particular object in the model, an example of overfitting would be that the model would not only generate images with this object, but also include elements of the background of the training images disrupting the visual consistency of the newly created picture. For many, baking a LoRA is an art more than a science and the choice of parameters is influenced by reports circulating on an extensive network of platforms that range from Reddit and [[CivitAI]] to GitHub and [[Hugging Face]] to Arxiv. At the training stage, the network extends from the sites that provide images and curatorial resources to a mix of highly technical pages and sites where informal conversation can be had.
Line 73:		Line 73:
	As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.		As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.

	The largest gain is in term of literacy and understanding of the training process more generally. Indeed as LoRAs are miniature models, the skills and expertise related to curation, sourcing, annotation, and model semantics are being developed through a peer to peer effort in communities of amateurs and image makers. This knowledge that initially pertained to niches of specialists is being popularized, shared and translated to numerous cultural contexts. If there are still many obstacles to a radical delinking from platforms, there are many encouraging signs that point to a potential convergence between communities and a less centrally controlled infrastructure. LoRA creation might not be the undoing of the centralizing power of hegemonic platforms, far from there. But it can be a step in that direction.		The largest gain is in term of literacy and understanding of the training process more generally. Indeed as LoRAs are miniature models, the skills and expertise related to curation, sourcing, annotation, and model semantics are being developed through a peer to peer effort in communities of amateurs and image makers. This knowledge that initially pertained to niches of specialists is being popularized, shared and translated to numerous cultural contexts. If there are still many obstacles to a radical delinking from platforms, there are many encouraging signs that point to a potential convergence between communities and a less centrally controlled infrastructure. LoRA creation might not be the undoing of the centralizing power of hegemonic platforms, far from there. But it can be a step in that direction.

	[1] BigHeadTF, ''The Incredible Hulk (2008) – V1'', Civitai, published February 18, 2025, accessed August 18, 2025, https://civitai.com/models/1266100/the-incredible-hulk-2008.		[1] BigHeadTF, ''The Incredible Hulk (2008) – V1'', Civitai, published February 18, 2025, accessed August 18, 2025, https://civitai.com/models/1266100/the-incredible-hulk-2008.

NicolasMaleve: /* How does it relate to autonomous infrastructure? */

2025-08-21T14:40:05Z

How does it relate to autonomous infrastructure?

← Older revision		Revision as of 16:40, 21 August 2025
Line 73:		Line 73:
	As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.		As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.

	The largest gain is in term of literacy and understanding of the training process more generally. Indeed as LoRAs are miniature models, the skills and expertise related to curation, sourcing, annotation, and model semantics are being developed through a peer to peer effort in communities of amateurs and image makers. This knowledge that initially pertained to niches of specialists is being popularized, shared and translated to numerous cultural contexts. If there are still many obstacles to a radical delinking from platforms, there are many encouraging signs that point to a potential convergence between communities and a less centrally controlled infrastructure. LoRA creation might not be the undoing of the centralizing power of hegemonic platforms, far from there. But it can be a step in that direction.		The largest gain is in term of literacy and understanding of the training process more generally. Indeed as LoRAs are miniature models, the skills and expertise related to curation, sourcing, annotation, and model semantics are being developed through a peer to peer effort in communities of amateurs and image makers. This knowledge that initially pertained to niches of specialists is being popularized, shared and translated to numerous cultural contexts. If there are still many obstacles to a radical delinking from platforms, there are many encouraging signs that point to a potential convergence between communities and a less centrally controlled infrastructure. LoRA creation might not be the undoing of the centralizing power of hegemonic platforms, far from there. But it can be a step in that direction.

	[1] BigHeadTF, ''The Incredible Hulk (2008) – V1'', Civitai, published February 18, 2025, accessed August 18, 2025, https://civitai.com/models/1266100/the-incredible-hulk-2008.		[1] BigHeadTF, ''The Incredible Hulk (2008) – V1'', Civitai, published February 18, 2025, accessed August 18, 2025, https://civitai.com/models/1266100/the-incredible-hulk-2008.
Line 85:		Line 85:
	[5] knxo, “Making a LoRA Is Like Baking a Cake.”		[5] knxo, “Making a LoRA Is Like Baking a Cake.”

	[6] Danbooru, "Dirt Road", ''Danbooru'', accessed August 18, 2025, https://danbooru.donmai.us/posts?tags=dirt_road		[6] Danbooru, "Dirt Road", ''Danbooru'', accessed August 18, 2025, https://danbooru.donmai.us/posts?tags=dirt_road

	[6] ~~IBM~~, ~~“What Is Overfitting?”~~ ''~~IBM Think~~'', ~~last modified October 15~~, ~~2021~~, ~~accessed August 18, 2025~~, https://~~www.ibm~~.com/~~think/topics~~/~~overfitting~~.		[7] Junnan Li, Khaliq Ahsen'', BLIP: Bootstrapping Language-Image Pre-training'', GitHub repository, Salesforce, 2022, https://github.com/salesforce/BLIP.

	[7] ~~Civitai~~, ~~“LoRA Training Glossary~~,” ~~''Civitai Education''~~, accessed August 18, 2025, https://~~education~~.~~civitai~~.~~com~~/~~lora-training-glossary~~/.		[8] Wikipedia, “Imageboard,” last modified August 2025, accessed August 18, 2025, https://en.wikipedia.org/wiki/Imageboard#Booru.

	[8] ~~tehalex86~~, ~~''5K CrazyWhatever'', Civitai, accessed August 18, 2025, https://civitai.com/bounties/8690/5k-crazywhatever~~.		[9] knxo, “Making a LoRA Is Like Baking a Cake.”

	[9] ~~Stagnation, “The Most Prolific Character LoRAs in Existence by FAR~~,” ''~~Civitai~~'', ~~published February 2~~, ~~2024~~, accessed August 18, 2025, https://~~civitai~~.com/~~articles~~/~~3940~~/~~the-most-prolific-character-loras-in-existence-by-far~~.		[10] IBM, “What Is Overfitting?” ''IBM Think'', last modified October 15, 2021, accessed August 18, 2025, https://www.ibm.com/think/topics/overfitting.

	[10] ~~ColonelJay~~, ~~"Multiple characters Bounty Results!"~~, ''Civitai''~~, published August 16, 2025~~, accessed August 21, 2025, https://civitai.com/~~articles/17883/multiple~~-~~characters~~-~~bounty-results~~		[11] Civitai, “LoRA Training Glossary,” ''Civitai Education'', accessed August 18, 2025, https://education.civitai.com/lora-training-glossary/.

	[11] ~~extrafuzzy, "How I made over $1000 in 3 months selling LoRAs"~~, ''~~Civitai~~'', ~~published October 22, 2023~~, accessed August 21, 2025, https://civitai.com/~~articles~~/~~2684~~/~~how~~-~~i-made-over-dollar1000-in-3-months-selling-loras~~		[12] tehalex86, ''5K CrazyWhatever'', Civitai, accessed August 18, 2025, https://civitai.com/bounties/8690/5k-crazywhatever.

	[12] ~~AI_Characters~~, ''~~Amateur Snapshot Photo Style LoRA – FLUX~~''~~, Civitai~~, published ~~June 26~~, ~~2025~~, accessed August 18, 2025, https://civitai.com/~~models~~/~~970862~~/~~amateur~~-~~snapshot~~-~~photo~~-~~style~~-~~lora~~-~~flux~~.		[13] Stagnation, “The Most Prolific Character LoRAs in Existence by FAR,” ''Civitai'', published February 2, 2024, accessed August 18, 2025, https://civitai.com/articles/3940/the-most-prolific-character-loras-in-existence-by-far.

	~~'''~~[13] ~~MISSING REFERENCE (~~"~~dirt_road~~ "~~on the Dabooru Wiki)!!!~~'''		[14] ColonelJay, "Multiple characters Bounty Results!", ''Civitai'', published August 16, 2025, accessed August 21, 2025, https://civitai.com/articles/17883/multiple-characters-bounty-results

	[14] ~~Junnan Li~~, ~~Khaliq Ahsen~~''~~, BLIP: Bootstrapping Language-Image Pre-training~~'', ~~GitHub repository, Salesforce, 2022, https://github.com/salesforce/BLIP.~~		[15] extrafuzzy, "How I made over $1000 in 3 months selling LoRAs", ''Civitai'', published October 22, 2023, accessed August 21, 2025, https://civitai.com/articles/2684/how-i-made-over-dollar1000-in-3-months-selling-loras.

	~~[15] Wikipedia, “Imageboard~~,~~” last modified August 2025~~, accessed August 18, 2025, https://en.~~wikipedia.org~~/~~wiki~~/~~Imageboard#Booru.~~

	~~[16] knxo, “Making a LoRA Is Like Baking a Cake.”~~

	~~+++++++++++++++++++++++++++++++++~~

	~~'''~~/~~/ SHOULD THIS BE INCLUDED IN THE ENTRY? ADD FOOTNOTES AT THE END; IF SO~~ - ~~AND A REFERENCE TO THE DIAGRAM //'''~~
	[[Category:Objects of Interest and Necessity]]		[[Category:Objects of Interest and Necessity]]

	~~=== Unused references ===~~
	*Free software libraries and apps
	** https://huggingface.co/docs/peft/index
	** https://github.com/bmaltais/kohya_ss/
	** https://civitai.com/articles/3168/lora-inspector
	* Tutorials and documentations
	** https://education.civitai.com/lora-training-glossary/
	** https://civitai.com/articles/4/make-your-own-loras-easy-and-free
	** https://civitai.com/articles/8310/lora-training-using-illustrious-report-by-a-beginner Since Illustrious learned on danbooru, she has memorized many of the Art Styles of the artists who have contributed to danbooru. Therefore, it is better to check if it is already learned Art Style you want to resemble, so that you do not have to waste your time. Note that I wasted several hours trying to create the Art Style of my favorite doujinshi and eroge illustrators
	~~Technical communities and Manga fans~~
	* https://civitai.com/articles/197/not-maintained-anymore-use-tag-search-instead-arknights-lora-collection-in-alphabetical-order
	* https://civitai.com/articles/166/lora-training-guide-rz-passage-21-15

NicolasMaleve: /* How does it relate to autonomous infrastructure? */

2025-08-21T14:33:22Z

How does it relate to autonomous infrastructure?

← Older revision		Revision as of 16:33, 21 August 2025
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	[5] knxo, “Making a LoRA Is Like Baking a Cake.”		[5] knxo, “Making a LoRA Is Like Baking a Cake.”

			[6] Danbooru, "Dirt Road", ''Danbooru'', accessed August 18, 2025, https://danbooru.donmai.us/posts?tags=dirt_road

	[6] IBM, “What Is Overfitting?” ''IBM Think'', last modified October 15, 2021, accessed August 18, 2025, https://www.ibm.com/think/topics/overfitting.		[6] IBM, “What Is Overfitting?” ''IBM Think'', last modified October 15, 2021, accessed August 18, 2025, https://www.ibm.com/think/topics/overfitting.

NicolasMaleve: /* How does it relate to autonomous infrastructure? */

2025-08-21T14:26:54Z

How does it relate to autonomous infrastructure?

← Older revision		Revision as of 16:26, 21 August 2025
Line 73:		Line 73:
	As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.		As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.

	The largest gain is in term of literacy and understanding of the training process more generally. Indeed as LoRAs are miniature models, the skills and expertise related to curation, sourcing, annotation, and model semantics are being developed through a peer to peer effort in communities of amateurs and image makers. This knowledge that initially pertained to niches of specialists is being popularized, shared and translated to numerous cultural contexts. If there are still many obstacles to a radical delinking from platforms, there are many encouraging signs that point to a potential convergence between communities and a less centrally controlled infrastructure. LoRA creation might not be the undoing of the centralizing power of hegemonic platforms, far from there. But it can be a step in that direction.		The largest gain is in term of literacy and understanding of the training process more generally. Indeed as LoRAs are miniature models, the skills and expertise related to curation, sourcing, annotation, and model semantics are being developed through a peer to peer effort in communities of amateurs and image makers. This knowledge that initially pertained to niches of specialists is being popularized, shared and translated to numerous cultural contexts. If there are still many obstacles to a radical delinking from platforms, there are many encouraging signs that point to a potential convergence between communities and a less centrally controlled infrastructure. LoRA creation might not be the undoing of the centralizing power of hegemonic platforms, far from there. But it can be a step in that direction.

	[1] BigHeadTF, ''The Incredible Hulk (2008) – V1'', Civitai, published February 18, 2025, accessed August 18, 2025, https://civitai.com/models/1266100/the-incredible-hulk-2008.		[1] BigHeadTF, ''The Incredible Hulk (2008) – V1'', Civitai, published February 18, 2025, accessed August 18, 2025, https://civitai.com/models/1266100/the-incredible-hulk-2008.

NicolasMaleve: /* Re-modelling as filling the gaps */

2025-08-21T14:26:40Z

Re-modelling as filling the gaps

← Older revision		Revision as of 16:26, 21 August 2025
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	=== What is its place/role in techno cultural strategies? ===		=== What is its place/role in techno cultural strategies? ===

	=~~=== Re-modelling~~ as ~~filling~~ the ~~gaps ====~~		[[File:Civitai-loras-q9.png\|none\|alt=Screengrab of the LoRA page on the civit.ai platform\|Screengrab of the LoRA page on the civit.ai platform\|thumb\|400x400px]]A quick look at the LoRA page on CivitAI gives an idea of the forms of gender representation that dominates the platform. When discussing the site's visuals, a participant to a workshop we gave in the context of Xeno Visual Studies in Madrid eructed "this is incel culture". And indeed a large portion of the LoRAs feature female characters with large breasts and bodybuilded male heroes. If parodies and critiques of these stereotypes also circulate on the platform as the opening vignette of this entry demonstrates, they remain limited in numbers.

	~~Baking a LoRA is a way~~ to ~~deal with one's dependency on existing~~ models. ~~It can be done~~ to ~~fill some gap in~~ the ~~model~~. ~~For instance, the~~ model ~~might not be able to generate convincingly~~ a ~~given anime character.~~		While we experimented ourselves with the creation of LoRAs, we wondered how we could begin responding to the lack of diversity in the representations of women in both LoRAs and models. Our small experiment took the form of a LoRA to represent the artist Louise Bourgeois. Indeed prompting a model such as Real Vision with a query such as "The artist Louise Bourgeois working in her studio" resulted in an image of an older woman dressed in a contemporary attire with a vague physical likeness to the artist. Assembling a dataset from online and offline images, we ended up with a dozen candidates which we annotated in the Draw Thing software locally. Subsequently we used Draw Things to train a LoRA with RealVision as our base model and explored the results.

	~~Experiments made by us. Changing the representation~~ of a ~~female~~ artist in the model. ~~Example of Louise Bourgeois experiment:~~

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	~~'''// NEEDS EDITING //''' Discuss difference between this experiment and gender representation on platforms such as CivitAI. Workshop in Madrid~~, ~~reaction to the list of most rated LoRAs "this is incel culture".~~		Interestingly, with the LoRA, the same model managed to produce a more resembling character. The facial expression with the ironic smile characterizing Bourgeois was present. The general texture of the artworks surrounding Bourgeois were also closer to her work although they remained more academic in style. Instead of showing her drawing at the studio's table, the model was now showing her in contact with the sculptures. Whilst this experiment remained limited and the portraits of Bourgeois were still rather crude, the potential for an engagement with the artist's representation clearly appeared to us. However, this left us with a difficult question. If there is an undeniable production of technical knowledge and skill and (as we develop below) a gain in autonomy from the practice of LoRA creation, how could this potential of technical emancipation be aligned with a different aesthetics and politics of representation?

	~~[[File:Civitai-loras-q9.png\|none\|alt=Screengrab of the LoRA page on the civit.ai platform\|Screengrab of the LoRA page on the civit.ai platform\|thumb\|400x400px]]~~

	~~It can also be done because the model's aesthetics are too present in the output. As a motivation for the creation of~~ the ~~Amateur Snapshot Photo~~ LoRA, the ~~user AI_Characters states:~~

	~~"This LoRa~~ model ~~was designed~~ to ~~generate images that look~~ more ~~"real" and less "artificially AI-generated by FLUX"~~. ~~It achieves this by making~~ the ~~natural and artificial lighting look more real and making the bokeh less strong~~. ~~It also adds details to every part~~ of the ~~image, including skin, eyes, hair, and foliage. It~~ also ~~aims~~ to ~~reduce somewhat the common "FLUX chin and skin" and other such issues~~.~~"[16]~~

	~~The Flux model is considered by many image makers as a state~~ of the ~~art tool. But as pointed above, it tends to imprint a specific aesthetics on the human characters it generates and produce pictures that are too~~ '~~clean'. Images bear its signature. Amateur Snapshot Photo~~, ~~like many other LoRAs, tries to mitigate these issues. The creation of~~ the ~~LoRA works both~~ with and ~~against~~ the ~~underlying model. The art~~ of ~~fine-tuning is to select~~ the ~~model that generates the closest results to what one strives to achieve and improve it. But also to limit~~ the ~~model~~'s ~~propensities~~. ~~This back and forth dialogue~~ with ~~the model requires~~ a ~~highly reflexive understanding of the underlying model's affordances~~. ~~It requires~~ an ~~intimate~~ knowledge of ~~the model's visual capabilities~~ and ~~its semantics.~~

	=== How does it relate to autonomous infrastructure? ===		=== How does it relate to autonomous infrastructure? ===
	The precondition for the existence of LoRA is the realisation that base models cannot generate everything, that they are partial and biased. This technique allows to regain partial control over the training, a part of the technical process that is usually reserved to a handful of companies. In that perspective, the existence of LoRAs evokes the possibility of a re-appropriation of the model via fine-tuning. Even if not complete, this helps users regain some form of autonomy from large models providers. ~~Needs~~ are defined bottom-up.		The precondition for the existence of LoRA is the realisation that base models cannot generate everything, that they are partial and biased. This technique allows to regain partial control over the training, a part of the technical process that is usually reserved to a handful of companies. In that perspective, the existence of LoRAs evokes the possibility of a re-appropriation of the model via fine-tuning. Even if not complete, this helps users regain some form of autonomy from large models providers. In particular because their needs are defined bottom-up.

	As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.		As written above, the interest for LoRAs corresponds for many users to the realisation that the interaction with AI generators is showing some limits. If these limits are first felt at the level of content, the journey of LoRA creation confronts the users to another important limitation: the availability of hardware. LoRA training provokes an engagement with the material plane of genAI ecosystem and its logistics. The materiality of the system becomes palpable either through its costs, or through alternative means of accessing a highly coveted GPU. LoRA creation makes the user aware of the genAI ecosystem economy as access to compute is 'linked' to different forms of [[currencies]]. Be it in platforms such as CivitAI, the buzz, or in networks such as Stable Horde, kudos. This being said, fine-tuning techniques benefit from the evolution of the material conditions. As the technical requirements become lighter, LoRA production can happen in different environments. In privileged countries, advanced users can train locally on their own machine with consumer-grade GPUs. With a small budget, LoRAs can be trained online. Infrastructurally speaking, edge AI and 'community' platforms are in a position to meet the needs for LoRA training and therefore decentralize a step further the training process. But to date, peer to peer networks such as Stable Horde are still limited to inference.

NicolasMaleve: /* A technical primer */

2025-08-21T13:19:46Z

A technical primer

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NicolasMaleve: /* How does it relate to autonomous infrastructure? */

2025-08-21T12:55:58Z

How does it relate to autonomous infrastructure?

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	'''[14] MISSING REFERENCE ("dirt_road "on the Dabooru Wiki)!!!'''		'''[14] MISSING REFERENCE ("dirt_road "on the Dabooru Wiki)!!!'''

	[15] Junnan Li, ''BLIP: Bootstrapping Language-Image Pre-training'', GitHub repository, Salesforce, 2022, https://github.com/salesforce/BLIP. ~~'''// IS JUNNAN LI CORRECT???//'''~~		[15] Junnan Li, Khaliq Ahsen'', BLIP: Bootstrapping Language-Image Pre-training'', GitHub repository, Salesforce, 2022, https://github.com/salesforce/BLIP.

	[16] Wikipedia, “Imageboard,” last modified August 2025, accessed August 18, 2025, https://en.wikipedia.org/wiki/Imageboard#Booru.		[16] Wikipedia, “Imageboard,” last modified August 2025, accessed August 18, 2025, https://en.wikipedia.org/wiki/Imageboard#Booru.

NicolasMaleve: /* How does it relate to autonomous infrastructure? */

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How does it relate to autonomous infrastructure?

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	[8] Civitai, “LoRA Training Glossary,” ''Civitai Education'', accessed August 18, 2025, https://education.civitai.com/lora-training-glossary/.		[8] Civitai, “LoRA Training Glossary,” ''Civitai Education'', accessed August 18, 2025, https://education.civitai.com/lora-training-glossary/.

	[9] tehalex86, ''5K CrazyWhatever'', Civitai, accessed August 18, 2025, https://civitai.com/bounties/8690/5k-crazywhatever. ~~'''//tehalex86 IS THE AUTHOR OF THE COMPETITION; RIGHT???///'''~~		[9] tehalex86, ''5K CrazyWhatever'', Civitai, accessed August 18, 2025, https://civitai.com/bounties/8690/5k-crazywhatever.

	[10] Stagnation, “The Most Prolific Character LoRAs in Existence by FAR,” ''Civitai'', published February 2, 2024, accessed August 18, 2025, https://civitai.com/articles/3940/the-most-prolific-character-loras-in-existence-by-far.		[10] Stagnation, “The Most Prolific Character LoRAs in Existence by FAR,” ''Civitai'', published February 2, 2024, accessed August 18, 2025, https://civitai.com/articles/3940/the-most-prolific-character-loras-in-existence-by-far.